Overview

Dataset statistics

Number of variables10
Number of observations3276
Missing cells1438
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory256.1 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Potability is highly imbalanced (52.1%)Imbalance
ph has 492 (15.0%) missing valuesMissing
Sulfate has 781 (23.8%) missing valuesMissing
Trihalomethanes has 162 (4.9%) missing valuesMissing
Hardness has unique valuesUnique
Chloramines has unique valuesUnique
Organic_carbon has unique valuesUnique
Turbidity has unique valuesUnique

Reproduction

Analysis started2023-11-17 08:49:57.454055
Analysis finished2023-11-17 08:50:06.072132
Duration8.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ph
Real number (ℝ)

Distinct2784
Distinct (%)100.0%
Missing492
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean7.0807659
Minimum0
Maximum14
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:06.146221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.4878574
Q16.0926646
median7.0358944
Q38.062251
95-th percentile9.7905459
Maximum14
Range14
Interquartile range (IQR)1.9695864

Descriptive statistics

Standard deviation1.5946052
Coefficient of variation (CV)0.22520236
Kurtosis0.71898834
Mean7.0807659
Median Absolute Deviation (MAD)0.98660881
Skewness0.025679719
Sum19712.852
Variance2.5427658
MonotonicityNot monotonic
2023-11-17T14:20:06.230575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.704431913 1
 
< 0.1%
5.91580675 1
 
< 0.1%
8.136497869 1
 
< 0.1%
6.493764175 1
 
< 0.1%
6.977405633 1
 
< 0.1%
5.489248055 1
 
< 0.1%
2.558102799 1
 
< 0.1%
7.312109304 1
 
< 0.1%
8.55409697 1
 
< 0.1%
8.028304242 1
 
< 0.1%
Other values (2774) 2774
84.7%
(Missing) 492
 
15.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.22749905 1
< 0.1%
0.97557799 1
< 0.1%
0.989912213 1
< 0.1%
1.431781555 1
< 0.1%
1.757037115 1
< 0.1%
1.844538366 1
< 0.1%
1.985383359 1
< 0.1%
2.128531434 1
< 0.1%
2.376768076 1
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
13.54124024 1
< 0.1%
13.34988856 1
< 0.1%
13.17540172 1
< 0.1%
12.24692807 1
< 0.1%
11.90773983 1
< 0.1%
11.89807803 1
< 0.1%
11.62114013 1
< 0.1%
11.56876797 1
< 0.1%
11.56316906 1
< 0.1%

Hardness
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.3695
Minimum47.432
Maximum323.124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:06.331819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum47.432
5-th percentile141.76328
Q1176.85054
median196.96763
Q3216.66746
95-th percentile249.60977
Maximum323.124
Range275.692
Interquartile range (IQR)39.816918

Descriptive statistics

Standard deviation32.879761
Coefficient of variation (CV)0.16743823
Kurtosis0.61577168
Mean196.3695
Median Absolute Deviation (MAD)19.844989
Skewness-0.039341705
Sum643306.47
Variance1081.0787
MonotonicityNot monotonic
2023-11-17T14:20:06.421993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204.8904555 1
 
< 0.1%
134.5602761 1
 
< 0.1%
170.1909123 1
 
< 0.1%
237.4610992 1
 
< 0.1%
171.2389255 1
 
< 0.1%
197.4281988 1
 
< 0.1%
195.7440741 1
 
< 0.1%
184.2318535 1
 
< 0.1%
187.8732835 1
 
< 0.1%
205.1505644 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
47.432 1
< 0.1%
73.49223369 1
< 0.1%
77.4595861 1
< 0.1%
81.71089527 1
< 0.1%
94.09130748 1
< 0.1%
94.81254522 1
< 0.1%
94.90897713 1
< 0.1%
97.2809086 1
< 0.1%
98.3679149 1
< 0.1%
98.45293051 1
< 0.1%
ValueCountFrequency (%)
323.124 1
< 0.1%
317.3381241 1
< 0.1%
311.3839565 1
< 0.1%
308.2538329 1
< 0.1%
307.7060241 1
< 0.1%
306.6274814 1
< 0.1%
304.2359121 1
< 0.1%
303.7026267 1
< 0.1%
300.2924758 1
< 0.1%
298.0986795 1
< 0.1%

Solids
Real number (ℝ)

Distinct3274
Distinct (%)100.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean22016.534
Minimum320.94261
Maximum61227.196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:06.513786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum320.94261
5-th percentile9545.6859
Q115663.524
median20939.067
Q327335.562
95-th percentile38477.621
Maximum61227.196
Range60906.253
Interquartile range (IQR)11672.039

Descriptive statistics

Standard deviation8770.673
Coefficient of variation (CV)0.39836756
Kurtosis0.44090487
Mean22016.534
Median Absolute Deviation (MAD)5807.0737
Skewness0.62079574
Sum72082133
Variance76924705
MonotonicityNot monotonic
2023-11-17T14:20:06.599869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20791.31898 1
 
< 0.1%
15979.33479 1
 
< 0.1%
37000.95567 1
 
< 0.1%
18736.1909 1
 
< 0.1%
12289.90092 1
 
< 0.1%
15979.06027 1
 
< 0.1%
12431.80311 1
 
< 0.1%
30031.83918 1
 
< 0.1%
29532.615 1
 
< 0.1%
19821.33837 1
 
< 0.1%
Other values (3264) 3264
99.6%
(Missing) 2
 
0.1%
ValueCountFrequency (%)
320.9426113 1
< 0.1%
728.7508296 1
< 0.1%
1198.943699 1
< 0.1%
1351.906979 1
< 0.1%
1372.091043 1
< 0.1%
2552.962804 1
< 0.1%
2808.025756 1
< 0.1%
2835.303165 1
< 0.1%
2912.211247 1
< 0.1%
3413.081633 1
< 0.1%
ValueCountFrequency (%)
61227.19601 1
< 0.1%
56867.85924 1
< 0.1%
56488.67241 1
< 0.1%
56351.3963 1
< 0.1%
56320.58698 1
< 0.1%
55334.7028 1
< 0.1%
53735.89919 1
< 0.1%
52318.9173 1
< 0.1%
52060.2268 1
< 0.1%
51731.82055 1
< 0.1%

Chloramines
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1231905
Minimum0.352
Maximum13.127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:06.690483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.352
5-th percentile4.5030537
Q16.1274208
median7.1310702
Q38.1154583
95-th percentile9.7539437
Maximum13.127
Range12.775
Interquartile range (IQR)1.9880375

Descriptive statistics

Standard deviation1.5839077
Coefficient of variation (CV)0.22235931
Kurtosis0.58601958
Mean7.1231905
Median Absolute Deviation (MAD)0.99317851
Skewness-0.011890107
Sum23335.572
Variance2.5087636
MonotonicityNot monotonic
2023-11-17T14:20:06.781894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.300211873 1
 
< 0.1%
9.504361027 1
 
< 0.1%
6.217222542 1
 
< 0.1%
5.599870342 1
 
< 0.1%
10.78649982 1
 
< 0.1%
7.424944591 1
 
< 0.1%
6.6616162 1
 
< 0.1%
6.21530731 1
 
< 0.1%
7.981036899 1
 
< 0.1%
6.344963412 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
0.352 1
< 0.1%
0.530351295 1
< 0.1%
1.390870905 1
< 0.1%
1.683992581 1
< 0.1%
1.920271449 1
< 0.1%
2.102690991 1
< 0.1%
2.386653494 1
< 0.1%
2.39798499 1
< 0.1%
2.456013596 1
< 0.1%
2.458609195 1
< 0.1%
ValueCountFrequency (%)
13.127 1
< 0.1%
13.04380611 1
< 0.1%
12.91218664 1
< 0.1%
12.65336202 1
< 0.1%
12.62689974 1
< 0.1%
12.58002649 1
< 0.1%
12.36328483 1
< 0.1%
12.27937418 1
< 0.1%
12.2463941 1
< 0.1%
12.22717528 1
< 0.1%

Sulfate
Real number (ℝ)

Distinct2495
Distinct (%)100.0%
Missing781
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean333.77578
Minimum129
Maximum481.03064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:06.870013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile266.61623
Q1307.6995
median333.07355
Q3359.95017
95-th percentile403.07019
Maximum481.03064
Range352.03064
Interquartile range (IQR)52.250673

Descriptive statistics

Standard deviation41.41684
Coefficient of variation (CV)0.12408582
Kurtosis0.64826282
Mean333.77578
Median Absolute Deviation (MAD)26.095176
Skewness-0.035946622
Sum832770.56
Variance1715.3547
MonotonicityNot monotonic
2023-11-17T14:20:06.953840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280.7456229 1
 
< 0.1%
332.7445192 1
 
< 0.1%
391.9182286 1
 
< 0.1%
330.9053704 1
 
< 0.1%
402.3134271 1
 
< 0.1%
360.6978151 1
 
< 0.1%
336.0404518 1
 
< 0.1%
405.5273372 1
 
< 0.1%
346.0636768 1
 
< 0.1%
368.5164413 1
 
< 0.1%
Other values (2485) 2485
75.9%
(Missing) 781
 
23.8%
ValueCountFrequency (%)
129 1
< 0.1%
180.2067464 1
< 0.1%
182.3973702 1
< 0.1%
187.1707144 1
< 0.1%
187.4241309 1
< 0.1%
192.0335917 1
< 0.1%
203.4445208 1
< 0.1%
205.9350906 1
< 0.1%
206.2472294 1
< 0.1%
207.8904823 1
< 0.1%
ValueCountFrequency (%)
481.0306423 1
< 0.1%
476.5397173 1
< 0.1%
475.7374602 1
< 0.1%
462.474215 1
< 0.1%
460.107069 1
< 0.1%
458.4410723 1
< 0.1%
455.4512337 1
< 0.1%
450.9144544 1
< 0.1%
449.2676875 1
< 0.1%
447.4179624 1
< 0.1%

Conductivity
Real number (ℝ)

Distinct3275
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean426.23638
Minimum181.48375
Maximum753.34262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:07.052091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum181.48375
5-th percentile300.10633
Q1365.78725
median421.89008
Q3481.81267
95-th percentile566.35133
Maximum753.34262
Range571.85887
Interquartile range (IQR)116.02543

Descriptive statistics

Standard deviation80.816584
Coefficient of variation (CV)0.18960508
Kurtosis-0.27644926
Mean426.23638
Median Absolute Deviation (MAD)57.868841
Skewness0.26410492
Sum1395924.1
Variance6531.3202
MonotonicityNot monotonic
2023-11-17T14:20:07.139319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
564.3086542 1
 
< 0.1%
418.6420628 1
 
< 0.1%
517.5767619 1
 
< 0.1%
235.0422835 1
 
< 0.1%
501.5597252 1
 
< 0.1%
452.1872326 1
 
< 0.1%
367.8540248 1
 
< 0.1%
400.6118991 1
 
< 0.1%
469.1321169 1
 
< 0.1%
482.5957093 1
 
< 0.1%
Other values (3265) 3265
99.7%
ValueCountFrequency (%)
181.483754 1
< 0.1%
201.6197368 1
< 0.1%
210.319182 1
< 0.1%
217.3583296 1
< 0.1%
232.613624 1
< 0.1%
233.9079651 1
< 0.1%
235.0422835 1
< 0.1%
245.859632 1
< 0.1%
247.9180305 1
< 0.1%
251.0208987 1
< 0.1%
ValueCountFrequency (%)
753.3426196 1
< 0.1%
708.2263645 1
< 0.1%
695.369528 1
< 0.1%
674.4434759 1
< 0.1%
672.5569992 1
< 0.1%
669.7250862 1
< 0.1%
666.6906183 1
< 0.1%
660.2549463 1
< 0.1%
657.5704218 1
< 0.1%
656.9241278 1
< 0.1%

Organic_carbon
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.288365
Minimum2.2
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:07.228887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.8153617
Q112.065801
median14.219418
Q316.559114
95-th percentile19.63865
Maximum28.3
Range26.1
Interquartile range (IQR)4.493313

Descriptive statistics

Standard deviation3.3136581
Coefficient of variation (CV)0.23191303
Kurtosis0.05817786
Mean14.288365
Median Absolute Deviation (MAD)2.2342364
Skewness0.032366677
Sum46808.684
Variance10.98033
MonotonicityNot monotonic
2023-11-17T14:20:07.319255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.37978308 1
 
< 0.1%
12.89763545 1
 
< 0.1%
15.87176979 1
 
< 0.1%
11.545477 1
 
< 0.1%
12.28433352 1
 
< 0.1%
18.58495937 1
 
< 0.1%
21.30064694 1
 
< 0.1%
15.28878163 1
 
< 0.1%
16.1692117 1
 
< 0.1%
12.16473568 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
2.2 1
< 0.1%
4.371898608 1
< 0.1%
4.466771969 1
< 0.1%
4.473092264 1
< 0.1%
4.861631498 1
< 0.1%
4.902888068 1
< 0.1%
4.966861619 1
< 0.1%
5.051694615 1
< 0.1%
5.159380308 1
< 0.1%
5.188466455 1
< 0.1%
ValueCountFrequency (%)
28.3 1
< 0.1%
27.00670661 1
< 0.1%
25.0000005 1
< 0.1%
24.75539237 1
< 0.1%
23.95245044 1
< 0.1%
23.91760126 1
< 0.1%
23.66766678 1
< 0.1%
23.60429797 1
< 0.1%
23.56964491 1
< 0.1%
23.51477377 1
< 0.1%

Trihalomethanes
Real number (ℝ)

Distinct3114
Distinct (%)100.0%
Missing162
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean66.396293
Minimum0.738
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:07.638361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.738
5-th percentile39.552928
Q155.844536
median66.622485
Q377.337473
95-th percentile92.124059
Maximum124
Range123.262
Interquartile range (IQR)21.492937

Descriptive statistics

Standard deviation16.175008
Coefficient of variation (CV)0.24361313
Kurtosis0.23859744
Mean66.396293
Median Absolute Deviation (MAD)10.742172
Skewness-0.083030674
Sum206758.06
Variance261.6309
MonotonicityNot monotonic
2023-11-17T14:20:07.729016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.99097046 1
 
< 0.1%
56.71550955 1
 
< 0.1%
77.73081437 1
 
< 0.1%
90.39489472 1
 
< 0.1%
37.78709664 1
 
< 0.1%
78.9255271 1
 
< 0.1%
89.47771837 1
 
< 0.1%
69.526718 1
 
< 0.1%
72.57395938 1
 
< 0.1%
57.78086932 1
 
< 0.1%
Other values (3104) 3104
94.7%
(Missing) 162
 
4.9%
ValueCountFrequency (%)
0.738 1
< 0.1%
8.175876384 1
< 0.1%
8.577012933 1
< 0.1%
14.34316145 1
< 0.1%
15.6848768 1
< 0.1%
16.2915046 1
< 0.1%
17.00068293 1
< 0.1%
17.52776496 1
< 0.1%
17.91572257 1
< 0.1%
18.01527236 1
< 0.1%
ValueCountFrequency (%)
124 1
< 0.1%
120.030077 1
< 0.1%
118.3572747 1
< 0.1%
116.1616216 1
< 0.1%
114.2086714 1
< 0.1%
114.0349457 1
< 0.1%
113.0488857 1
< 0.1%
112.622733 1
< 0.1%
112.4122104 1
< 0.1%
112.0610274 1
< 0.1%

Turbidity
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9667862
Minimum1.45
Maximum6.739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-11-17T14:20:07.818288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile2.6842792
Q13.4397109
median3.9550276
Q34.5003198
95-th percentile5.2209245
Maximum6.739
Range5.289
Interquartile range (IQR)1.0606089

Descriptive statistics

Standard deviation0.78038241
Coefficient of variation (CV)0.19672913
Kurtosis-0.062800641
Mean3.9667862
Median Absolute Deviation (MAD)0.53029624
Skewness-0.0078166424
Sum12995.191
Variance0.6089967
MonotonicityNot monotonic
2023-11-17T14:20:07.921221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.963135381 1
 
< 0.1%
3.987012091 1
 
< 0.1%
4.066229364 1
 
< 0.1%
3.759326201 1
 
< 0.1%
4.876273 1
 
< 0.1%
5.143750122 1
 
< 0.1%
4.513200539 1
 
< 0.1%
4.20418585 1
 
< 0.1%
4.586748359 1
 
< 0.1%
4.910911021 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
1.45 1
< 0.1%
1.492206615 1
< 0.1%
1.496100943 1
< 0.1%
1.64151501 1
< 0.1%
1.659799385 1
< 0.1%
1.680554025 1
< 0.1%
1.687624505 1
< 0.1%
1.801326999 1
< 0.1%
1.81252894 1
< 0.1%
1.844371604 1
< 0.1%
ValueCountFrequency (%)
6.739 1
< 0.1%
6.494748556 1
< 0.1%
6.494249467 1
< 0.1%
6.389161009 1
< 0.1%
6.35743852 1
< 0.1%
6.307678472 1
< 0.1%
6.226580405 1
< 0.1%
6.204846359 1
< 0.1%
6.099631873 1
< 0.1%
6.083772354 1
< 0.1%

Potability
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.7 KiB
0
2938 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3276
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2938
89.7%
1 338
 
10.3%

Length

2023-11-17T14:20:08.010175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T14:20:08.089843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2938
89.7%
1 338
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 2938
89.7%
1 338
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3276
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2938
89.7%
1 338
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3276
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2938
89.7%
1 338
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2938
89.7%
1 338
 
10.3%

Interactions

2023-11-17T14:20:05.025204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.026906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.928598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.692088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.718630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.636711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.405342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.211835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.988055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.101313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.147234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.015197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.775358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.817250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.725299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.492178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.297168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.360914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.182975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.271317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.100326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.857711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.912321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.811386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.579727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.380317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.443403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.263195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.407403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.186152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.941664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.044427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.899210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.660879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.466081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.534461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.347113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.517087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.265272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.035983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.170067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.984752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.742442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.545916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.617840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.426629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.605187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.352501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.124558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.267837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.071786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.850138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.625196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.701221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.502857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.689248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.436471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.208744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.374779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.157307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.953757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.702456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.785609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.578572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.769726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.523355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.315267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.459804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.235586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.035834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.778855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.861888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:05.661153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:58.851788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:19:59.604788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:00.421006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:01.552180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:02.319884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.118183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:03.874990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-17T14:20:04.943750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-17T14:20:08.150221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
ph1.0000.116-0.075-0.0430.0240.0180.0450.005-0.0490.054
Hardness0.1161.000-0.053-0.025-0.095-0.0330.004-0.012-0.0130.077
Solids-0.075-0.0531.000-0.054-0.1540.0210.018-0.0200.0290.057
Chloramines-0.043-0.025-0.0541.0000.036-0.017-0.0130.017-0.0080.038
Sulfate0.024-0.095-0.1540.0361.000-0.0220.021-0.031-0.0190.000
Conductivity0.018-0.0330.021-0.017-0.0221.0000.022-0.0050.0110.000
Organic_carbon0.0450.0040.018-0.0130.0210.0221.000-0.008-0.0240.000
Trihalomethanes0.005-0.012-0.0200.017-0.031-0.005-0.0081.000-0.0280.026
Turbidity-0.049-0.0130.029-0.008-0.0190.011-0.024-0.0281.0000.018
Potability0.0540.0770.0570.0380.0000.0000.0000.0260.0181.000

Missing values

2023-11-17T14:20:05.770589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T14:20:05.887765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-17T14:20:06.015403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
0NaN204.89045620791.318987.300212368.516441564.30865410.37978386.9909702.9631350
13.716080129.42292118630.057866.635246NaN592.88535915.18001356.3290764.5006560
28.099124224.23625919909.541739.275884NaN418.60621316.86863766.4200933.0559340
38.316766214.37339422018.417448.059332356.886136363.26651618.436525100.3416744.6287710
49.092223181.10150917978.986346.546600310.135738398.41081311.55827931.9979934.0750750
55.584087188.31332428748.687747.544869326.678363280.4679168.39973554.9178622.5597080
610.223862248.07173528749.716547.513408393.663395283.65163413.78969584.6035562.6729890
78.635849203.36152313672.091764.563009303.309771474.60764512.36381762.7983094.4014250
8NaN118.98857914285.583857.804174268.646941389.37556612.70604953.9288463.5950170
911.180284227.23146925484.508499.077200404.041635563.88548117.92780671.9766014.3705620
phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
32668.372910169.08705214622.745497.547984NaN464.52555211.08302738.4351514.9063581
32678.989900215.04735815921.412026.297312312.931021390.4102319.89911555.0693044.6138431
32686.702547207.32108617246.920357.708117304.510230329.26600216.21730328.8786013.4429831
326911.49101194.81254537188.826029.263166258.930600439.89361816.17275541.5585014.3692641
32706.069616186.65904026138.780197.747547345.700257415.88695512.06762060.4199213.6697121
32714.668102193.68173647580.991607.166639359.948574526.42417113.89441966.6876954.4358211
32727.808856193.55321217329.802168.061362NaN392.44958019.903225NaN2.7982431
32739.419510175.76264633155.578227.350233NaN432.04478311.03907069.8454003.2988751
32745.126763230.60375811983.869386.303357NaN402.88311311.16894677.4882134.7086581
32757.874671195.10229917404.177067.509306NaN327.45976116.14036878.6984462.3091491